On-Line Analytical Processing (OLAP) is a category of software technology that enables insight into enterprise data through access to a wide variety of views of the enterprise data. Enterprise data is a large collection of business data, such as historical sales data of commercial items based on such attributes as location, market, product, weather, etc. With the large amount of data available, an analyst typically seeks to discern trends or relationships in the business data, for example, how many units of a product sold over the summer in three Midwestern states. Typically, such a query in enterprise data is a laborious task. OLAP seeks to reduce the amount of time involved by pre-calculating common types of queries. The analyst uses the OLAP results to rapidly evaluate the desired historical relationships in data at a more meaningful level. OLAP reduces the enterprise data granularity by aggregating the enterprise data into larger aggregations. For example, if the enterprise data breaks down products sales at the store level for a particular chain, an OLAP pre-calculated query may only return the product sales for the chain.
OLAP has been used to analyze dependent data, such as, but not limited to, sales volume of product(s), revenue, profits, etc. The data for OLAP is typically organized into a volume cube representing sales volume of a product for different locations (or markets, depending on the granularity of the resulting aggregated volume data). OLAP operates across two large, general classes of data: dependent and causal. Dependent data is data that is determined by the values of the causal data. For example, sales volume of a product is a market at a point in time that may be the result of causal data (e.g. price, weather, advertising, etc.). Furthermore, OLAP uses causal data to develop insights into the factor affecting dependent data, such as product volume. OLAP simultaneously aggregates or determines dependent and causal data. For example, if OLAP aggregated volume in three Midwestern states, OLAP should also calculate an aggregate, or average price in those states. Causal data is a collection of data (e.g. price, advertising, weather, etc.) that affects the dependent data (e.g., sales, revenue, profits, etc.). OLAP is useful to an analyst because it provides the base data from which analysts may make their own predictions of future data by understanding past trends or relationships and drawing conclusions about the future through inference.
However, OLAP typically analyzes past trends and not future trends, because OLAP assumes the existence of historical data in the form of dependent and causal data in order to perform its analyses. In addition, OLAP reduces dependent data granularity by aggregating the dependent data with pre-calculated queries.